Working analysis

Survey questions

Q1. Before receiving this survey, did you know influenza is different from the stomach flu?

# Q1 summary
with(data2, table(Q1))
## Q1
##   No  Yes 
##  488 1664
q1 <- data2 %>%
  count(Q1)

# plot with this one
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = Q1))

# ggplot(q1, aes(x = Q1, y = n, fill = Q1)) + geom_bar(stat = 'identity')

# plot without na's
#ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
#  geom_bar(stat = 'identity', position = position_dodge())



# by gender, PPGENDER
with(data2, table(PPGENDER, Q1))
##         Q1
## PPGENDER  No Yes
##   Female 205 888
##   Male   283 776
q1 <- data2 %>%
  count(Q1, PPGENDER)

# plot
ggplot(data2[!is.na(data2$Q1), ]) + geom_bar(mapping = aes(x = Q1, fill = PPGENDER), position = position_dodge())

# ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPGENDER)) +
#   geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPGENDER)

# by ethnicity, PPETHM
with(data2, table(PPETHM, Q1))
##                         Q1
## PPETHM                     No  Yes
##   2+ Races, Non-Hispanic   18   62
##   Black, Non-Hispanic      50  143
##   Hispanic                 69  161
##   Other, Non-Hispanic      29   63
##   White, Non-Hispanic     322 1235
q1 <- data2 %>%
  count(Q1, PPETHM)

# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPETHM)

# by income, PPINCIMP
with(data2, table(PPINCIMP, Q1))
##                       Q1
## PPINCIMP                No Yes
##   Less than $5,000      22  30
##   $5,000 to $7,499       8  16
##   $7,500 to $9,999       7   7
##   $10,000 to $12,499    17  39
##   $12,500 to $14,999    10  38
##   $15,000 to $19,999    22  40
##   $20,000 to $24,999    16  55
##   $25,000 to $29,999    23  76
##   $30,000 to $34,999    21  70
##   $35,000 to $39,999    31  72
##   $40,000 to $49,999    42 107
##   $50,000 to $59,999    46 137
##   $60,000 to $74,999    50 172
##   $75,000 to $84,999    26 133
##   $85,000 to $99,999    33 120
##   $100,000 to $124,999  56 269
##   $125,000 to $149,999  24 108
##   $150,000 to $174,999  16  68
##   $175,000 or more      18 107
q1 <- data2 %>%
  count(Q1, PPINCIMP)

# plot
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

# plot with facet
ggplot(q1[!is.na(q1$Q1), ], aes(x = Q1, y = n, fill = Q1)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~PPINCIMP)

Q2. Have you had an illness with influenza-like symptoms since August 2015?

#
with(data2, table(Q2))
## Q2
##   No  Yes 
## 1735  414
q2 <- data2 %>%
  count(Q2)
ggplot(q2, aes(x = Q2, y = n, fill = Q2)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(Q2, PPGENDER))
##      PPGENDER
## Q2    Female Male
##   No     858  877
##   Yes    234  180
q2 <- data2 %>%
  count(Q2, PPGENDER)
ggplot(q2, aes(x = Q2, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity
with(data2, table(Q2, PPETHM))
##      PPETHM
## Q2    2+ Races, Non-Hispanic Black, Non-Hispanic Hispanic
##   No                      61                 152      164
##   Yes                     19                  39       65
##      PPETHM
## Q2    Other, Non-Hispanic White, Non-Hispanic
##   No                   71                1287
##   Yes                  22                 269
q2 <- data2 %>%
  count(Q2, PPETHM)
ggplot(q2, aes(x = Q2, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income
with(data2, table(Q2, PPINCIMP))
##      PPINCIMP
## Q2    Less than $5,000 $5,000 to $7,499 $7,500 to $9,999
##   No                43               19               13
##   Yes                9                6                1
##      PPINCIMP
## Q2    $10,000 to $12,499 $12,500 to $14,999 $15,000 to $19,999
##   No                  38                 39                 46
##   Yes                 17                  9                 15
##      PPINCIMP
## Q2    $20,000 to $24,999 $25,000 to $29,999 $30,000 to $34,999
##   No                  55                 79                 74
##   Yes                 17                 19                 18
##      PPINCIMP
## Q2    $35,000 to $39,999 $40,000 to $49,999 $50,000 to $59,999
##   No                  85                121                155
##   Yes                 18                 27                 27
##      PPINCIMP
## Q2    $60,000 to $74,999 $75,000 to $84,999 $85,000 to $99,999
##   No                 172                130                123
##   Yes                 50                 29                 29
##      PPINCIMP
## Q2    $100,000 to $124,999 $125,000 to $149,999 $150,000 to $174,999
##   No                   265                  112                   62
##   Yes                   61                   20                   21
##      PPINCIMP
## Q2    $175,000 or more
##   No               104
##   Yes               21
q2 <- data2 %>%
  count(Q2, PPINCIMP)
ggplot(q2, aes(x = Q2, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q3. Has any other person in your household had an illness with influenza like symptoms since August 2015?

# all
with(data2, table(Q3))
## Q3
## Don_t know         No        Yes 
##        161       1608        383
q3 <- data2 %>%
  count(Q3)
ggplot(q3, aes(x = Q3, y = n, fill = Q3)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(Q3, PPGENDER))
##             PPGENDER
## Q3           Female Male
##   Don_t know     72   89
##   No            804  804
##   Yes           217  166
q3 <- data2 %>%
  count(Q3, PPGENDER)
ggplot(q3, aes(x = Q3, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity
with(data2, table(Q3, PPETHM))
##             PPETHM
## Q3           2+ Races, Non-Hispanic Black, Non-Hispanic Hispanic
##   Don_t know                      6                  19       30
##   No                             57                 149      146
##   Yes                            17                  25       53
##             PPETHM
## Q3           Other, Non-Hispanic White, Non-Hispanic
##   Don_t know                  11                  95
##   No                          59                1197
##   Yes                         23                 265
q3 <- data2 %>%
  count(Q3, PPETHM)
ggplot(q3, aes(x = Q3, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income
with(data2, table(Q3, PPINCIMP))
##             PPINCIMP
## Q3           Less than $5,000 $5,000 to $7,499 $7,500 to $9,999
##   Don_t know               11                6                1
##   No                       36               18               13
##   Yes                       5                1                0
##             PPINCIMP
## Q3           $10,000 to $12,499 $12,500 to $14,999 $15,000 to $19,999
##   Don_t know                  4                  7                  7
##   No                         44                 30                 47
##   Yes                         8                 11                  8
##             PPINCIMP
## Q3           $20,000 to $24,999 $25,000 to $29,999 $30,000 to $34,999
##   Don_t know                  8                  4                 11
##   No                         52                 81                 70
##   Yes                        12                 13                  9
##             PPINCIMP
## Q3           $35,000 to $39,999 $40,000 to $49,999 $50,000 to $59,999
##   Don_t know                 11                  6                 13
##   No                         75                117                136
##   Yes                        17                 25                 33
##             PPINCIMP
## Q3           $60,000 to $74,999 $75,000 to $84,999 $85,000 to $99,999
##   Don_t know                 18                  7                 11
##   No                        165                120                107
##   Yes                        39                 33                 35
##             PPINCIMP
## Q3           $100,000 to $124,999 $125,000 to $149,999
##   Don_t know                   20                    6
##   No                          245                  100
##   Yes                          61                   26
##             PPINCIMP
## Q3           $150,000 to $174,999 $175,000 or more
##   Don_t know                    3                7
##   No                           58               94
##   Yes                          23               24
q3 <- data2 %>%
  count(Q3, PPINCIMP)
ggplot(q3, aes(x = Q3, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q4. Does your job require you to have a lot of contact with the public?

# all
with(data2, table(Q4))
## Q4
##                                         No, I don_t work 
##                                                      779 
## No, my job does not require much contact with the public 
##                                                      620 
##                                                      Yes 
##                                                      751
(
q4 <- data2 %>%
  count(Q4)
)
## Source: local data frame [4 x 2]
## 
##                                                         Q4     n
##                                                      <chr> <int>
## 1                                         No, I don_t work   779
## 2 No, my job does not require much contact with the public   620
## 3                                                      Yes   751
## 4                                                       NA    18
ggplot(q4, aes(x = Q4, y = n, fill = Q4)) + geom_bar(stat = 'identity') +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by gender
with(data2, table(Q4, PPGENDER))
##                                                           PPGENDER
## Q4                                                         Female Male
##   No, I don_t work                                            430  349
##   No, my job does not require much contact with the public    263  357
##   Yes                                                         400  351
q4 <- data2 %>%
  count(Q4, PPGENDER)
ggplot(q4, aes(x = Q4, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by ethnicity
with(data2, table(Q4, PPETHM))
##                                                           PPETHM
## Q4                                                         2+ Races, Non-Hispanic
##   No, I don_t work                                                             30
##   No, my job does not require much contact with the public                     23
##   Yes                                                                          27
##                                                           PPETHM
## Q4                                                         Black, Non-Hispanic
##   No, I don_t work                                                          69
##   No, my job does not require much contact with the public                  59
##   Yes                                                                       64
##                                                           PPETHM
## Q4                                                         Hispanic
##   No, I don_t work                                               69
##   No, my job does not require much contact with the public       72
##   Yes                                                            87
##                                                           PPETHM
## Q4                                                         Other, Non-Hispanic
##   No, I don_t work                                                          24
##   No, my job does not require much contact with the public                  34
##   Yes                                                                       35
##                                                           PPETHM
## Q4                                                         White, Non-Hispanic
##   No, I don_t work                                                         587
##   No, my job does not require much contact with the public                 432
##   Yes                                                                      538
q4 <- data2 %>%
  count(Q4, PPETHM)
ggplot(q4, aes(x = Q4, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by income 
with(data2, table(Q4, PPINCIMP))
##                                                           PPINCIMP
## Q4                                                         Less than $5,000
##   No, I don_t work                                                       29
##   No, my job does not require much contact with the public               17
##   Yes                                                                     6
##                                                           PPINCIMP
## Q4                                                         $5,000 to $7,499
##   No, I don_t work                                                       15
##   No, my job does not require much contact with the public                5
##   Yes                                                                     5
##                                                           PPINCIMP
## Q4                                                         $7,500 to $9,999
##   No, I don_t work                                                       11
##   No, my job does not require much contact with the public                1
##   Yes                                                                     2
##                                                           PPINCIMP
## Q4                                                         $10,000 to $12,499
##   No, I don_t work                                                         33
##   No, my job does not require much contact with the public                  7
##   Yes                                                                      15
##                                                           PPINCIMP
## Q4                                                         $12,500 to $14,999
##   No, I don_t work                                                         32
##   No, my job does not require much contact with the public                  5
##   Yes                                                                      11
##                                                           PPINCIMP
## Q4                                                         $15,000 to $19,999
##   No, I don_t work                                                         28
##   No, my job does not require much contact with the public                 13
##   Yes                                                                      21
##                                                           PPINCIMP
## Q4                                                         $20,000 to $24,999
##   No, I don_t work                                                         35
##   No, my job does not require much contact with the public                 18
##   Yes                                                                      19
##                                                           PPINCIMP
## Q4                                                         $25,000 to $29,999
##   No, I don_t work                                                         46
##   No, my job does not require much contact with the public                 15
##   Yes                                                                      37
##                                                           PPINCIMP
## Q4                                                         $30,000 to $34,999
##   No, I don_t work                                                         38
##   No, my job does not require much contact with the public                 25
##   Yes                                                                      29
##                                                           PPINCIMP
## Q4                                                         $35,000 to $39,999
##   No, I don_t work                                                         42
##   No, my job does not require much contact with the public                 22
##   Yes                                                                      39
##                                                           PPINCIMP
## Q4                                                         $40,000 to $49,999
##   No, I don_t work                                                         64
##   No, my job does not require much contact with the public                 41
##   Yes                                                                      43
##                                                           PPINCIMP
## Q4                                                         $50,000 to $59,999
##   No, I don_t work                                                         60
##   No, my job does not require much contact with the public                 58
##   Yes                                                                      63
##                                                           PPINCIMP
## Q4                                                         $60,000 to $74,999
##   No, I don_t work                                                         73
##   No, my job does not require much contact with the public                 60
##   Yes                                                                      88
##                                                           PPINCIMP
## Q4                                                         $75,000 to $84,999
##   No, I don_t work                                                         45
##   No, my job does not require much contact with the public                 51
##   Yes                                                                      64
##                                                           PPINCIMP
## Q4                                                         $85,000 to $99,999
##   No, I don_t work                                                         47
##   No, my job does not require much contact with the public                 48
##   Yes                                                                      58
##                                                           PPINCIMP
## Q4                                                         $100,000 to $124,999
##   No, I don_t work                                                           87
##   No, my job does not require much contact with the public                  111
##   Yes                                                                       127
##                                                           PPINCIMP
## Q4                                                         $125,000 to $149,999
##   No, I don_t work                                                           39
##   No, my job does not require much contact with the public                   51
##   Yes                                                                        42
##                                                           PPINCIMP
## Q4                                                         $150,000 to $174,999
##   No, I don_t work                                                           23
##   No, my job does not require much contact with the public                   25
##   Yes                                                                        36
##                                                           PPINCIMP
## Q4                                                         $175,000 or more
##   No, I don_t work                                                       32
##   No, my job does not require much contact with the public               47
##   Yes                                                                    46
q4 <- data2 %>%
  count(Q4, PPINCIMP)
ggplot(q4, aes(x = Q4, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Q5. Do you have a car that you can use to travel to work?

# all
with(data2, table(Q5))
## Q5
##   No  Yes 
##  133 1235
q5 <- data2 %>%
  count(Q5)
ggplot(q5, aes(x = Q5, y = n, fill = Q5)) + geom_bar(stat = 'identity')

# by gender
with(data2, table(PPGENDER, Q5))
##         Q5
## PPGENDER  No Yes
##   Female  70 592
##   Male    63 643
q5 <- data2 %>%
  count(Q5, PPGENDER)
ggplot(q5, aes(x = Q5, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity 
q5 <- data2 %>%
  count(Q5, PPETHM)
ggplot(q5, aes(x = Q5, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income 
q5 <- data2 %>%
  count(Q5, PPINCIMP)
ggplot(q5, aes(x = Q5, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q6. Do you regularly use public transportation?

# all
with(data2, table(Q6))
## Q6
##   No  Yes 
## 1959  194
q6 <- data2 %>%
  count(Q6)
ggplot(q6, aes(x = Q6, y = n, fill = Q6)) + geom_bar(stat = 'identity')

# by gender
# with(data2, table(PPGENDER, Q6))
(q6 <- data2 %>%
  count(Q6, PPGENDER)
)
## Source: local data frame [6 x 3]
## Groups: Q6 [?]
## 
##      Q6 PPGENDER     n
##   (chr)    (chr) (int)
## 1    No   Female   998
## 2    No     Male   961
## 3   Yes   Female    96
## 4   Yes     Male    98
## 5    NA   Female     3
## 6    NA     Male    12
ggplot(q6, aes(x = Q6, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by ethnicity 
(q6 <- data2 %>%
  count(Q6, PPETHM)
)
## Source: local data frame [13 x 3]
## Groups: Q6 [?]
## 
##       Q6                 PPETHM     n
##    (chr)                  (chr) (int)
## 1     No 2+ Races, Non-Hispanic    62
## 2     No    Black, Non-Hispanic   158
## 3     No               Hispanic   196
## 4     No    Other, Non-Hispanic    80
## 5     No    White, Non-Hispanic  1463
## 6    Yes 2+ Races, Non-Hispanic    18
## 7    Yes    Black, Non-Hispanic    36
## 8    Yes               Hispanic    32
## 9    Yes    Other, Non-Hispanic    13
## 10   Yes    White, Non-Hispanic    95
## 11    NA    Black, Non-Hispanic     1
## 12    NA               Hispanic     4
## 13    NA    White, Non-Hispanic    10
ggplot(q6, aes(x = Q6, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge())

# by income 
(q6 <- data2 %>%
  count(Q6, PPINCIMP)
)
## Source: local data frame [50 x 3]
## Groups: Q6 [?]
## 
##       Q6           PPINCIMP     n
##    (chr)             (fctr) (int)
## 1     No   Less than $5,000    42
## 2     No   $5,000 to $7,499    22
## 3     No   $7,500 to $9,999    10
## 4     No $10,000 to $12,499    47
## 5     No $12,500 to $14,999    42
## 6     No $15,000 to $19,999    58
## 7     No $20,000 to $24,999    64
## 8     No $25,000 to $29,999    90
## 9     No $30,000 to $34,999    85
## 10    No $35,000 to $39,999    92
## ..   ...                ...   ...
ggplot(q6, aes(x = Q6, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge())

Q7. What types of public transportation do you regularly use?

Q7 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, Q7_1_Bus:Q7_otherText) %>%
  gather("q", "r", Q7_1_Bus:Q7_7_Other)


# Q7
with(Q7, table(q, r))
##                r
## q                No Yes
##   Q7_1_Bus       57 137
##   Q7_2_Carpool  184  10
##   Q7_3_Subway   131  63
##   Q7_4_Train    139  55
##   Q7_5_Taxi     169  25
##   Q7_6_Airplane 175  19
##   Q7_7_Other    179  15
q7 <- Q7 %>%
  count(q, r)

# flip coordinates
ggplot(q7[!is.na(q7$r), ], aes(x = r, y = n, fill = r)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) + coord_flip()

# by gender
# with(Q7, table(PPGENDER, r, q))
(q7 <- Q7 %>%
  group_by(PPGENDER, q, r) %>%
  count(PPGENDER, q, r)
)
## Source: local data frame [42 x 4]
## Groups: PPGENDER, q [?]
## 
##    PPGENDER            q     r     n
##       (chr)        (chr) (chr) (int)
## 1    Female     Q7_1_Bus    No    27
## 2    Female     Q7_1_Bus   Yes    69
## 3    Female     Q7_1_Bus    NA  1001
## 4    Female Q7_2_Carpool    No    91
## 5    Female Q7_2_Carpool   Yes     5
## 6    Female Q7_2_Carpool    NA  1001
## 7    Female  Q7_3_Subway    No    68
## 8    Female  Q7_3_Subway   Yes    28
## 9    Female  Q7_3_Subway    NA  1001
## 10   Female   Q7_4_Train    No    75
## ..      ...          ...   ...   ...
ggplot(q7[!is.na(q7$r), ], aes(x = r, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q)

# by ethnicity
# with(Q7, table(PPETHM, r, q))
(q7 <- Q7 %>%
  group_by(PPETHM, q, r) %>%
  count(PPETHM, q, r)
)
## Source: local data frame [100 x 4]
## Groups: PPETHM, q [?]
## 
##                    PPETHM            q     r     n
##                     (chr)        (chr) (chr) (int)
## 1  2+ Races, Non-Hispanic     Q7_1_Bus    No     4
## 2  2+ Races, Non-Hispanic     Q7_1_Bus   Yes    14
## 3  2+ Races, Non-Hispanic     Q7_1_Bus    NA    62
## 4  2+ Races, Non-Hispanic Q7_2_Carpool    No    18
## 5  2+ Races, Non-Hispanic Q7_2_Carpool    NA    62
## 6  2+ Races, Non-Hispanic  Q7_3_Subway    No    12
## 7  2+ Races, Non-Hispanic  Q7_3_Subway   Yes     6
## 8  2+ Races, Non-Hispanic  Q7_3_Subway    NA    62
## 9  2+ Races, Non-Hispanic   Q7_4_Train    No    15
## 10 2+ Races, Non-Hispanic   Q7_4_Train   Yes     3
## ..                    ...          ...   ...   ...
ggplot(q7[!is.na(q7$r), ], aes(x = r, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q)

# by income
# with(Q7, table(q, r, PPINCIMP))
(q7 <- Q7 %>%
  group_by(PPINCIMP, q, r) %>%
  count(PPINCIMP, q, r)
)
## Source: local data frame [357 x 4]
## Groups: PPINCIMP, q [?]
## 
##            PPINCIMP            q     r     n
##              (fctr)        (chr) (chr) (int)
## 1  Less than $5,000     Q7_1_Bus   Yes    10
## 2  Less than $5,000     Q7_1_Bus    NA    43
## 3  Less than $5,000 Q7_2_Carpool    No    10
## 4  Less than $5,000 Q7_2_Carpool    NA    43
## 5  Less than $5,000  Q7_3_Subway    No     9
## 6  Less than $5,000  Q7_3_Subway   Yes     1
## 7  Less than $5,000  Q7_3_Subway    NA    43
## 8  Less than $5,000   Q7_4_Train    No     8
## 9  Less than $5,000   Q7_4_Train   Yes     2
## 10 Less than $5,000   Q7_4_Train    NA    43
## ..              ...          ...   ...   ...
ggplot(q7[!is.na(q7$r), ], aes(x = r, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q)

Q8. For what types of activities do you regularly use public transportation?

Q8 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, Q8_1_Work:Q8_otherText) %>%
  gather("q", "r", Q8_1_Work:Q8_6_Other)

with(Q8, table(q, r))
##                       r
## q                       No Yes
##   Q8_1_Work             89 105
##   Q8_2_School          158  36
##   Q8_3_Shopping        107  87
##   Q8_4_Visiting people 125  69
##   Q8_5_Recreation      127  67
##   Q8_6_Other           175  19
q8 <- Q8 %>%
  count(q, r)

Q9. Do other members of your household regularly use public transportation?

with(data2, table(Q9))
## Q9
## Don_t know         No        Yes 
##         32       1935        183

Q10. What types of public transportation do other members of your household regularly use?

Q10 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, Q10_1_Bus:Q10_9_Refused) %>%
  gather("q", "r", Q10_1_Bus:Q10_8_Other)

with(Q10, table(q, r))
##                   r
## q                   No Yes
##   Q10_1_Bus         48 135
##   Q10_2_Carpool    166  17
##   Q10_3_Subway     130  53
##   Q10_4_Train      137  46
##   Q10_5_Taxi       157  26
##   Q10_6_Airplane   164  19
##   Q10_7_Don_t know 182   1
##   Q10_8_Other      172  11
q10 <- Q10 %>%
  count(q, r)

Q11. How do you rate your risk of getting influenza if you visited each of the following locations?

Q11 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, Q11_1_Work:Q11_OtherText_Codes) %>%
  gather("q", "r", Q11_1_Work:Q11_11_Other)


# all
with(Q11, table(q, r))
##                              r
## q                             Don_t Know High Risk, Very Likely
##   Q11_1_Work                         185                    524
##   Q11_10_Family or friends           121                    541
##   Q11_11_Other                       915                     51
##   Q11_2_Schools                      178                    909
##   Q11_3_Day care                     214                    924
##   Q11_4_Stores                       115                    551
##   Q11_5_Restaurants                  111                    483
##   Q11_6_Libraries                    169                    386
##   Q11_7_Hospitals                    123                    982
##   Q11_8_Doctor_s office              110                    994
##   Q11_9_Public transportation        147                   1093
##                              r
## q                             Low Risk, Not Likely
##   Q11_1_Work                                   643
##   Q11_10_Family or friends                     485
##   Q11_11_Other                                 104
##   Q11_2_Schools                                508
##   Q11_3_Day care                               554
##   Q11_4_Stores                                 405
##   Q11_5_Restaurants                            442
##   Q11_6_Libraries                              700
##   Q11_7_Hospitals                              374
##   Q11_8_Doctor_s office                        308
##   Q11_9_Public transportation                  353
##                              r
## q                             Medium Risk, Somewhat Likely
##   Q11_1_Work                                           795
##   Q11_10_Family or friends                            1000
##   Q11_11_Other                                          54
##   Q11_2_Schools                                        551
##   Q11_3_Day care                                       454
##   Q11_4_Stores                                        1076
##   Q11_5_Restaurants                                   1111
##   Q11_6_Libraries                                      890
##   Q11_7_Hospitals                                      669
##   Q11_8_Doctor_s office                                733
##   Q11_9_Public transportation                          551
q11 <- Q11 %>%
  count(q, r)
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = r)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by gender
# with(Q7, table(PPGENDER, r, q))
(q11 <- Q11 %>%
  group_by(PPGENDER, q, r) %>%
  count(PPGENDER, q, r)
)
## Source: local data frame [110 x 4]
## Groups: PPGENDER, q [?]
## 
##    PPGENDER                        q                            r     n
##       (chr)                    (chr)                        (chr) (int)
## 1    Female               Q11_1_Work                   Don_t Know    89
## 2    Female               Q11_1_Work       High Risk, Very Likely   309
## 3    Female               Q11_1_Work         Low Risk, Not Likely   310
## 4    Female               Q11_1_Work Medium Risk, Somewhat Likely   381
## 5    Female               Q11_1_Work                           NA     8
## 6    Female Q11_10_Family or friends                   Don_t Know    53
## 7    Female Q11_10_Family or friends       High Risk, Very Likely   302
## 8    Female Q11_10_Family or friends         Low Risk, Not Likely   229
## 9    Female Q11_10_Family or friends Medium Risk, Somewhat Likely   506
## 10   Female Q11_10_Family or friends                           NA     7
## ..      ...                      ...                          ...   ...
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPGENDER)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by ethnicity
# with(Q7, table(PPETHM, r, q))
(q11 <- Q11 %>%
  group_by(PPETHM, q, r) %>%
  count(PPETHM, q, r)
)
## Source: local data frame [275 x 4]
## Groups: PPETHM, q [?]
## 
##                    PPETHM                        q
##                     (chr)                    (chr)
## 1  2+ Races, Non-Hispanic               Q11_1_Work
## 2  2+ Races, Non-Hispanic               Q11_1_Work
## 3  2+ Races, Non-Hispanic               Q11_1_Work
## 4  2+ Races, Non-Hispanic               Q11_1_Work
## 5  2+ Races, Non-Hispanic               Q11_1_Work
## 6  2+ Races, Non-Hispanic Q11_10_Family or friends
## 7  2+ Races, Non-Hispanic Q11_10_Family or friends
## 8  2+ Races, Non-Hispanic Q11_10_Family or friends
## 9  2+ Races, Non-Hispanic Q11_10_Family or friends
## 10 2+ Races, Non-Hispanic Q11_10_Family or friends
## ..                    ...                      ...
## Variables not shown: r (chr), n (int)
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPETHM)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# by income
# with(Q7, table(q, r, PPINCIMP))
(q11 <- Q11 %>%
  group_by(PPINCIMP, q, r) %>%
  count(PPINCIMP, q, r)
)
## Source: local data frame [985 x 4]
## Groups: PPINCIMP, q [?]
## 
##            PPINCIMP                        q                            r
##              (fctr)                    (chr)                        (chr)
## 1  Less than $5,000               Q11_1_Work                   Don_t Know
## 2  Less than $5,000               Q11_1_Work       High Risk, Very Likely
## 3  Less than $5,000               Q11_1_Work         Low Risk, Not Likely
## 4  Less than $5,000               Q11_1_Work Medium Risk, Somewhat Likely
## 5  Less than $5,000               Q11_1_Work                           NA
## 6  Less than $5,000 Q11_10_Family or friends                   Don_t Know
## 7  Less than $5,000 Q11_10_Family or friends       High Risk, Very Likely
## 8  Less than $5,000 Q11_10_Family or friends         Low Risk, Not Likely
## 9  Less than $5,000 Q11_10_Family or friends Medium Risk, Somewhat Likely
## 10 Less than $5,000 Q11_10_Family or friends                           NA
## ..              ...                      ...                          ...
## Variables not shown: n (int)
ggplot(q11[!is.na(q11$r), ], aes(x = r, y = n, fill = PPINCIMP)) +
  geom_bar(stat = 'identity', position = position_dodge()) + facet_wrap(~q) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Q12. Which of the following actions do you take to avoid getting sick?

Q12 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 75:91) %>%
  gather("q", "r", 7:21)

with(Q12, table(q, r))
##                                                      r
## q                                                     Always Never
##   Q12_1_Avoid touching my eyes                           653   324
##   Q12_10_Get recommended vaccine                        1041   564
##   Q12_11_Take preventive medicine                        425   831
##   Q12_12_Cover my nose and mouth with a surgical mask    218  1568
##   Q12_13_Avoid contact with people who are sick          765   153
##   Q12_14_Avoid crowded places                            406   413
##   Q12_15_Other                                            91   472
##   Q12_2_Avoid touching my nose                           613   349
##   Q12_3_Avoid touching my mouth                          758   300
##   Q12_4_Wash my hands with soap more often              1774    52
##   Q12_5_Use hand sanitizers                              911   278
##   Q12_6_Clean the surfaces in my home                   1132   115
##   Q12_7_Clean the surfaces at work                       752   544
##   Q12_8_Eat nutritious food                              895   107
##   Q12_9_Get adequate rest                                899   114
##                                                      r
## q                                                     Sometimes
##   Q12_1_Avoid touching my eyes                             1168
##   Q12_10_Get recommended vaccine                            540
##   Q12_11_Take preventive medicine                           890
##   Q12_12_Cover my nose and mouth with a surgical mask       358
##   Q12_13_Avoid contact with people who are sick            1228
##   Q12_14_Avoid crowded places                              1322
##   Q12_15_Other                                               87
##   Q12_2_Avoid touching my nose                             1183
##   Q12_3_Avoid touching my mouth                            1085
##   Q12_4_Wash my hands with soap more often                  317
##   Q12_5_Use hand sanitizers                                 957
##   Q12_6_Clean the surfaces in my home                       899
##   Q12_7_Clean the surfaces at work                          842
##   Q12_8_Eat nutritious food                                1144
##   Q12_9_Get adequate rest                                  1130
q12 <- Q12 %>%
  count(q, r)

Q13. Do you get the flu vaccine?

with(data2, table(Q13))
## Q13
##       No, never Yes, every year Yes, some years 
##             819             908             423
ggplot(data2[!is.na(data2$Q13), ]) + geom_bar(mapping = aes(x = Q13, fill = Q13), position = position_dodge())

Q14. How much do you pay to get an influenza vaccine?

with(data2, table(Q14))
## Q14
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           970            54            80           222             4
ggplot(data2[!is.na(data2$Q14), ]) + geom_bar(mapping = aes(x = Q14, fill = Q14), position = position_dodge())

# by gender
with(data2, by(Q14, PPGENDER, summary))
## PPGENDER: Female
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           514            28            41           101             2 
##          NA's 
##           411 
## -------------------------------------------------------- 
## PPGENDER: Male
##            $0    $30 to $60    Don_t know Less than $30 More than $60 
##           456            26            39           121             2 
##          NA's 
##           427

Q15. Are you more likely to get a vaccine if others around you get a vaccine?

with(data2, table(Q15))
## Q15
##  No, less likely    No, no effect Yes, more likely 
##               70              878              381
ggplot(data2[!is.na(data2$Q15), ]) + geom_bar(mapping = aes(x = Q15, fill = Q15), position = position_dodge())

Q16. Are you more likely to get a vaccine if others around you do not get a vaccine?

with(data2, table(Q16))
## Q16
##  No, less likely    No, no effect Yes, more likely 
##              101              904              313
ggplot(data2[!is.na(data2$Q16), ]) + geom_bar(mapping = aes(x = Q16, fill = Q16), position = position_dodge())

Q17. Do you get a vaccine to protect yourself, protect others, or protect yourself and others?

with(data2, table(Q17))
## Q17
##            Protect myself Protect myself and others 
##                       381                       921 
##            Protect others 
##                        22
ggplot(data2[!is.na(data2$Q17), ]) + geom_bar(mapping = aes(x = Q17, fill = Q17), position = position_dodge())

Q18. What are the reasons you would not get an influenza vaccine?

Q18 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 97:108) %>%
  gather("q", "r", 7:Q18_10_Other)

with(Q18, table(q, r))
##                                                                  r
## q                                                                   No
##   Q18_1_The vaccine costs too much                                1132
##   Q18_10_Other                                                    1064
##   Q18_2_The vaccine is not very effective in preventing influenza  903
##   Q18_3_I am not likely to get influenza                           964
##   Q18_4_Do not know where to get vaccine                          1199
##   Q18_5_The side effect of the vaccine are too risky               958
##   Q18_6_I am allergic to some of the ingredients in the vaccine   1184
##   Q18_7_I do not like shots                                        976
##   Q18_8_I just don_t get around to doing it                        878
##   Q18_9_I have to travel too far to get vaccine                   1216
##                                                                  r
## q                                                                  Yes
##   Q18_1_The vaccine costs too much                                 110
##   Q18_10_Other                                                     178
##   Q18_2_The vaccine is not very effective in preventing influenza  339
##   Q18_3_I am not likely to get influenza                           278
##   Q18_4_Do not know where to get vaccine                            43
##   Q18_5_The side effect of the vaccine are too risky               284
##   Q18_6_I am allergic to some of the ingredients in the vaccine     58
##   Q18_7_I do not like shots                                        266
##   Q18_8_I just don_t get around to doing it                        364
##   Q18_9_I have to travel too far to get vaccine                     26
q18 <- Q18 %>%
  count(q, r)

Q19. Do you have health insurance?

with(data2, table(Q19))
## Q19
##   No  Yes 
##  154 1994
ggplot(data2[!is.na(data2$Q19), ]) + geom_bar(mapping = aes(x = Q19, fill = Q19), position = position_dodge())

Q20. How effective do you think the influenza vaccine is in protecting people from becoming sick with influenza?

with(data2, table(Q20))
## Q20
##                      Don_t know It varies from season to season 
##                             228                             433 
##                   Not effective              Somewhat effective 
##                             144                             961 
##                  Very effective 
##                             383
ggplot(data2[!is.na(data2$Q20), ]) + geom_bar(mapping = aes(x = Q20, fill = Q20), position = position_dodge())

Q21. Are influenza vaccines covered by your health insurance?

with(data2, table(Q21))
## Q21
##                             Don_t know 
##                                    500 
##                                     No 
##                                     55 
## Yes, but only part of the cost is paid 
##                                    153 
##             Yes, the full cost is paid 
##                                   1282
ggplot(data2[!is.na(data2$Q21), ]) + geom_bar(mapping = aes(x = Q21, fill = Q21), position = position_dodge())

Q22. Do you do any of the following when you have influenza symptoms?

Q22 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 112:122) %>%
  gather("q", "r", 7:Q22_9_Other)

with(Q22, table(q, r))
##                                                                     r
## q                                                                    Always
##   Q22_1_Go to a doctor_s office or medical clinic                       349
##   Q22_2_Decide on treatment without consulting a health practitioner    335
##   Q22_3_Search the internet for a treatment                             126
##   Q22_4_Get adequate sleep                                             1147
##   Q22_5_Eat nutritious food                                             909
##   Q22_6_Take-over-counter medication for symptoms                       796
##   Q22_7_Take an antiviral medicine                                      153
##   Q22_8_Take no action to treat the illness                              96
##   Q22_9_Other                                                            54
##                                                                     r
## q                                                                    Never
##   Q22_1_Go to a doctor_s office or medical clinic                      552
##   Q22_2_Decide on treatment without consulting a health practitioner   473
##   Q22_3_Search the internet for a treatment                           1148
##   Q22_4_Get adequate sleep                                             115
##   Q22_5_Eat nutritious food                                            135
##   Q22_6_Take-over-counter medication for symptoms                      210
##   Q22_7_Take an antiviral medicine                                    1103
##   Q22_8_Take no action to treat the illness                           1199
##   Q22_9_Other                                                          448
##                                                                     r
## q                                                                    Sometimes
##   Q22_1_Go to a doctor_s office or medical clinic                         1235
##   Q22_2_Decide on treatment without consulting a health practitioner      1329
##   Q22_3_Search the internet for a treatment                                861
##   Q22_4_Get adequate sleep                                                 875
##   Q22_5_Eat nutritious food                                               1091
##   Q22_6_Take-over-counter medication for symptoms                         1130
##   Q22_7_Take an antiviral medicine                                         877
##   Q22_8_Take no action to treat the illness                                839
##   Q22_9_Other                                                               38
q22 <- Q22 %>%
  count(q, r)

Q23. Which of the following actions do you take when you have influenza symptoms to avoid someone else from getting sick?

Q23 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 123:Q23_11_Other) %>%
  gather("q", "r", 7:Q23_11_Other)

with(Q23, table(q, r))
##                                                        r
## q                                                       Always Never
##   Q23_1_Stand away from people                            1006   135
##   Q23_10_Cover my nose and mouth when I sneeze or cough   1717    81
##   Q23_11_Other                                              54   421
##   Q23_2_Avoid public places                                897   196
##   Q23_3_Avoid public transportation                       1342   245
##   Q23_4_Stay at home                                       869   163
##   Q23_5_Wash my hands with soap more often                1559    92
##   Q23_6_Use hand sanitizers                               1014   299
##   Q23_7_Clean the surfaces in my home                     1151   153
##   Q23_8_Clean the surfaces I use at work                   856   508
##   Q23_9_Cover my nose and mouth with a surgical mask       267  1463
##                                                        r
## q                                                       Sometimes
##   Q23_1_Stand away from people                                996
##   Q23_10_Cover my nose and mouth when I sneeze or cough       341
##   Q23_11_Other                                                 28
##   Q23_2_Avoid public places                                  1044
##   Q23_3_Avoid public transportation                           550
##   Q23_4_Stay at home                                         1106
##   Q23_5_Wash my hands with soap more often                    488
##   Q23_6_Use hand sanitizers                                   825
##   Q23_7_Clean the surfaces in my home                         832
##   Q23_8_Clean the surfaces I use at work                      772
##   Q23_9_Cover my nose and mouth with a surgical mask          409
q23 <- Q23 %>%
  count(q, r)

Q24. What sources of information do you recall hearing or seeing about influenza outbreaks?

Q24 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 137:Q24_7_Refused) %>%
  gather("q", "r", 7:Q24_6_Other)

with(Q24, table(q, r))
##                                                       r
## q                                                        No  Yes
##   Q24_1_Print media such as newspapers and magazines   1460  708
##   Q24_2_Traditional media such as television and radio  811 1357
##   Q24_3_Social media such as internet and blogs        1680  488
##   Q24_4_Word of mouth                                  1213  955
##   Q24_5_None                                           1764  404
##   Q24_6_Other                                          2114   54
q24 <- Q24 %>%
  count(q, r)

Q25. If you received information from the news, internet or other public media that there was an influenza outbreak in your community would you do any of the following?

Q25 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 145:Q25_11_Other) %>%
  gather("q", "r", 7:Q25_11_Other)

with(Q25, table(q, r))
##                                                        r
## q                                                       Always Never
##   Q25_1_Stand away from people                             649   217
##   Q25_10_Cover my nose and mouth when I sneeze or cough   1643    90
##   Q25_11_Other                                              32   393
##   Q25_2_Avoid public places                                648   270
##   Q25_3_Avoid public transportation                       1221   268
##   Q25_4_Stay at home                                       484   429
##   Q25_5_Wash my hands with soap more often                1477    99
##   Q25_6_Use hand sanitizers                               1077   257
##   Q25_7_Clean the surfaces in my home                     1116   160
##   Q25_8_Clean the surfaces I use at work                   902   464
##   Q25_9_Cover my nose and mouth with a surgical mask       343  1286
##                                                        r
## q                                                       Sometimes
##   Q25_1_Stand away from people                               1268
##   Q25_10_Cover my nose and mouth when I sneeze or cough       399
##   Q25_11_Other                                                 21
##   Q25_2_Avoid public places                                  1217
##   Q25_3_Avoid public transportation                           643
##   Q25_4_Stay at home                                         1222
##   Q25_5_Wash my hands with soap more often                    554
##   Q25_6_Use hand sanitizers                                   799
##   Q25_7_Clean the surfaces in my home                         857
##   Q25_8_Clean the surfaces I use at work                      766
##   Q25_9_Cover my nose and mouth with a surgical mask          505
q25 <- Q25 %>%
  count(q, r)

Q26. Does your household have children?

with(data2, table(Q26))
## Q26
##   No  Yes 
## 1570  576
ggplot(data2[!is.na(data2$Q26), ]) + geom_bar(mapping = aes(x = Q26, fill = Q26), position = position_dodge())

Q27. What actions do you take when a child in your household has influenza symptoms?

Q27 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 159:Q27_4_Other) %>%
  gather("q", "r", 7:Q27_4_Other)

with(Q27, table(q, r))
##                                                             r
## q                                                            Always Never
##   Q27_1_Keep the child away from the others in the residence    198    90
##   Q27_2_Keep the child out of school/daycare                    377    46
##   Q27_3_Stop child_s social activities like play dates          388    41
##   Q27_4_Other                                                    12    93
##                                                             r
## q                                                            Sometimes
##   Q27_1_Keep the child away from the others in the residence       285
##   Q27_2_Keep the child out of school/daycare                       149
##   Q27_3_Stop child_s social activities like play dates             144
##   Q27_4_Other                                                       12
q27 <- Q27 %>%
  count(q, r)

Q28. Are you a single parent?

with(data2, table(Q28))
## Q28
##  No Yes 
## 490  86
ggplot(data2[!is.na(data2$Q28), ]) + geom_bar(mapping = aes(x = Q28, fill = Q28), position = position_dodge())

Q29. How do you care for a sick child?

Q29 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 166:Q29_6_Other) %>%
  gather("q", "r", 7:Q29_6_Other)

with(Q29, table(q, r))
##                                                r
## q                                               Always Never Sometimes
##   Q29_1_A parent brings the child to work            7   438        41
##   Q29_2_A parent stays home                        266    27       193
##   Q29_3_Another adult stays home                    68   202       216
##   Q29_4_Send the child to school sick                1   414        70
##   Q29_5_Take the child to a relative or friends      8   292       186
##   Q29_6_Other                                        4    76         6
q29 <- Q29 %>%
  count(q, r)

Q30. How do you care for a sick child?

Q30 <- data2 %>%
  select(PPGENDER, PPAGE, PPEDUC, PPETHM, PPINCIMP, PPWORK, 174:Q30_6_Other) %>%
  gather("q", "r", 7:Q30_6_Other)

with(Q30, table(q, r))
##                                                r
## q                                               Always Never Sometimes
##   Q30_1_I bring the child to work                    4    77         5
##   Q30_2_I stay home                                 34    10        42
##   Q30_3_Another adult stays home                     9    25        52
##   Q30_4_Send the child to school sick                3    60        23
##   Q30_5_Take the child to a relative or friends      7    33        46
##   Q30_6_Other                                        1    14         3
q30 <- Q30 %>%
  count(q, r)

Q31. How many hours of screen time (time spent watching television, a computer, smartphone, iPad, etc.) do you spend each day on average when you are not sick? Enter 0 if none

with(data2, summary(Q31))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   4.868   6.000  24.000      52
# by gender
with(data2, by(Q31, PPGENDER, summary))
## PPGENDER: Female
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   4.838   6.000  21.000      21 
## -------------------------------------------------------- 
## PPGENDER: Male
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   4.898   6.000  24.000      31

Q32. How many hours of screen time do you spend each day on average when you are sick? Enter 0 if none

with(data2, summary(Q32))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   4.267   6.000  24.000      61
# by gender
with(data2, by(Q33, PPGENDER, summary))
## PPGENDER: Female
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   2.000   2.567   3.000   9.000       8 
## -------------------------------------------------------- 
## PPGENDER: Male
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   2.000   2.594   3.000  14.000      20

Q33. How many people, including yourself, reside in your household?

with(data2, summary(Q33))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    2.00    2.00    2.58    3.00   14.00      28
# by ethnicity
with(data2, by(Q33, PPETHM, summary))
## PPETHM: 2+ Races, Non-Hispanic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   2.000   2.709   3.000   7.000       1 
## -------------------------------------------------------- 
## PPETHM: Black, Non-Hispanic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   1.000   2.000   2.544   3.000  13.000       2 
## -------------------------------------------------------- 
## PPETHM: Hispanic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   3.000   2.903   4.000   9.000       6 
## -------------------------------------------------------- 
## PPETHM: Other, Non-Hispanic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   3.000   2.946   4.000   7.000       1 
## -------------------------------------------------------- 
## PPETHM: White, Non-Hispanic
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   2.000   2.509   3.000  14.000      18

Household Members

HHM1

Q35. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.

with(data2, table(Q35))
## Q35
## Female   Male 
##    799    859

Q36. On average, how many days per week does this member of your household work or attend day care or school outside of your home?

with(data2, summary(Q36))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   4.000   2.874   5.000   7.000     571

Q37. On average, how many days per week does this member of your household participate in social activities outside of your home?

with(data2, summary(Q37))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   2.098   3.000   7.000     663

Q38. On average, how many days per week does this member of your household use public transportation?

with(data2, summary(Q38))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.3909  0.0000  7.0000     582

Q39. How frequently does this member of your household visit a doctor’s office for wellness appointments?

with(data2, summary(Q39))
##    Length     Class      Mode 
##      2168 character character

Q40. How frequently does this member of the household get sick in a typical year?

with(data2, summary(Q40))
##    Length     Class      Mode 
##      2168 character character

Q41. How many times has this member of your household had influenza or another respiratory illness in the last two years?

with(data2, summary(Q41))
##    Length     Class      Mode 
##      2168 character character

Q42. Does this member of your household get an annual influenza vaccine?

with(data2, summary(Q42))
##    Length     Class      Mode 
##      2168 character character

HHM2

Q43. What is the gender of this member of the household? Remember, this relates to HHM1_Name who is HHM1_AGE years old.

with(data2, summary(Q43))
##    Length     Class      Mode 
##      2168 character character

Q44. On average, how many days per week does this member of your household work or attend day care or school outside of your home?

with(data2, summary(Q44))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   5.000   3.669   5.000   7.000    1383

Q45. On average, how many days per week does this member of your household participate in social activities outside of your home?

with(data2, summary(Q45))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   2.000   2.395   4.000   7.000    1419

Q46. On average, how many days per week does this member of your household use public transportation?

with(data2, summary(Q46))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5727  0.0000  7.0000    1391

Q47. How frequently does this member of your household visit a doctor’s office for wellness appointments?

with(data2, summary(Q47))
##    Length     Class      Mode 
##      2168 character character

Q48. How frequently does this member of the household get sick in a typical year?

with(data2, summary(Q48))
##    Length     Class      Mode 
##      2168 character character

Q49. How many times has this member of your household had influenza or another respiratory illness in the last two years?

with(data2, summary(Q49))
##    Length     Class      Mode 
##      2168 character character

Q50. Does this member of your household get an annual influenza vaccine?

with(data2, summary(Q50))
##    Length     Class      Mode 
##      2168 character character